






'''simple docstring'''







import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase     :   List[str]	      =		logging.get_logger(__name__)
def     A__      (					__lowerCAmelCase		:      Dict    ):
						lowerCamelCase__								=	DPTConfig()

						if "large" in checkpoint_url:
												lowerCamelCase__								=	1024
												lowerCamelCase__								=	4096
												lowerCamelCase__								=	24
												lowerCamelCase__								=	16
												lowerCamelCase__								=	[5, 11, 17, 23]
												lowerCamelCase__								=	[256, 512, 1024, 1024]
												lowerCamelCase__								=	(1, 384, 384)

						if "ade" in checkpoint_url:
												lowerCamelCase__								=	True

												lowerCamelCase__								=	150
												lowerCamelCase__								=	"""huggingface/label-files"""
												lowerCamelCase__								=	"""ade20k-id2label.json"""
												lowerCamelCase__								=	json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     repo_type="""dataset"""    )    ) ,     """r"""    )    )
												lowerCamelCase__								=	{int(SCREAMING_SNAKE_CASE__    ): v for k, v in idalabel.items()}
												lowerCamelCase__								=	idalabel
												lowerCamelCase__								=	{v: k for k, v in idalabel.items()}
												lowerCamelCase__								=	[1, 150, 480, 480]

						return config, expected_shape
def     A__      (					__lowerCAmelCase		:      Union[str, Any]    ):
						lowerCamelCase__								=	["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
						for k in ignore_keys:
												state_dict.pop(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )
def     A__      (					__lowerCAmelCase		:      int    ):





						if (
						    "pretrained.model" in name
						    and "cls_token" not in name
						    and "pos_embed" not in name
						    and "patch_embed" not in name
						):
												lowerCamelCase__								=	name.replace("""pretrained.model""" ,     """dpt.encoder"""    )
						if "pretrained.model" in name:
												lowerCamelCase__								=	name.replace("""pretrained.model""" ,     """dpt.embeddings"""    )
						if "patch_embed" in name:
												lowerCamelCase__								=	name.replace("""patch_embed""" ,     """patch_embeddings"""    )
						if "pos_embed" in name:
												lowerCamelCase__								=	name.replace("""pos_embed""" ,     """position_embeddings"""    )
						if "attn.proj" in name:
												lowerCamelCase__								=	name.replace("""attn.proj""" ,     """attention.output.dense"""    )
						if "proj" in name and "project" not in name:
												lowerCamelCase__								=	name.replace("""proj""" ,     """projection"""    )
						if "blocks" in name:
												lowerCamelCase__								=	name.replace("""blocks""" ,     """layer"""    )
						if "mlp.fc1" in name:
												lowerCamelCase__								=	name.replace("""mlp.fc1""" ,     """intermediate.dense"""    )
						if "mlp.fc2" in name:
												lowerCamelCase__								=	name.replace("""mlp.fc2""" ,     """output.dense"""    )
						if "norm1" in name:
												lowerCamelCase__								=	name.replace("""norm1""" ,     """layernorm_before"""    )
						if "norm2" in name:
												lowerCamelCase__								=	name.replace("""norm2""" ,     """layernorm_after"""    )
						if "scratch.output_conv" in name:
												lowerCamelCase__								=	name.replace("""scratch.output_conv""" ,     """head"""    )
						if "scratch" in name:
												lowerCamelCase__								=	name.replace("""scratch""" ,     """neck"""    )
						if "layer1_rn" in name:
												lowerCamelCase__								=	name.replace("""layer1_rn""" ,     """convs.0"""    )
						if "layer2_rn" in name:
												lowerCamelCase__								=	name.replace("""layer2_rn""" ,     """convs.1"""    )
						if "layer3_rn" in name:
												lowerCamelCase__								=	name.replace("""layer3_rn""" ,     """convs.2"""    )
						if "layer4_rn" in name:
												lowerCamelCase__								=	name.replace("""layer4_rn""" ,     """convs.3"""    )
						if "refinenet" in name:
												lowerCamelCase__								=	int(name[len("""neck.refinenet"""    ) : len("""neck.refinenet"""    ) + 1]    )
												# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
												lowerCamelCase__								=	name.replace(F'''refinenet{layer_idx}''' ,     F'''fusion_stage.layers.{abs(layer_idx-4    )}'''    )
						if "out_conv" in name:
												lowerCamelCase__								=	name.replace("""out_conv""" ,     """projection"""    )
						if "resConfUnit1" in name:
												lowerCamelCase__								=	name.replace("""resConfUnit1""" ,     """residual_layer1"""    )
						if "resConfUnit2" in name:
												lowerCamelCase__								=	name.replace("""resConfUnit2""" ,     """residual_layer2"""    )
						if "conv1" in name:
												lowerCamelCase__								=	name.replace("""conv1""" ,     """convolution1"""    )
						if "conv2" in name:
												lowerCamelCase__								=	name.replace("""conv2""" ,     """convolution2"""    )
						# readout blocks
						if "pretrained.act_postprocess1.0.project.0" in name:
												lowerCamelCase__								=	name.replace("""pretrained.act_postprocess1.0.project.0""" ,     """neck.reassemble_stage.readout_projects.0.0"""    )
						if "pretrained.act_postprocess2.0.project.0" in name:
												lowerCamelCase__								=	name.replace("""pretrained.act_postprocess2.0.project.0""" ,     """neck.reassemble_stage.readout_projects.1.0"""    )
						if "pretrained.act_postprocess3.0.project.0" in name:
												lowerCamelCase__								=	name.replace("""pretrained.act_postprocess3.0.project.0""" ,     """neck.reassemble_stage.readout_projects.2.0"""    )
						if "pretrained.act_postprocess4.0.project.0" in name:
												lowerCamelCase__								=	name.replace("""pretrained.act_postprocess4.0.project.0""" ,     """neck.reassemble_stage.readout_projects.3.0"""    )
						# resize blocks
						if "pretrained.act_postprocess1.3" in name:
												lowerCamelCase__								=	name.replace("""pretrained.act_postprocess1.3""" ,     """neck.reassemble_stage.layers.0.projection"""    )
						if "pretrained.act_postprocess1.4" in name:
												lowerCamelCase__								=	name.replace("""pretrained.act_postprocess1.4""" ,     """neck.reassemble_stage.layers.0.resize"""    )
						if "pretrained.act_postprocess2.3" in name:
												lowerCamelCase__								=	name.replace("""pretrained.act_postprocess2.3""" ,     """neck.reassemble_stage.layers.1.projection"""    )
						if "pretrained.act_postprocess2.4" in name:
												lowerCamelCase__								=	name.replace("""pretrained.act_postprocess2.4""" ,     """neck.reassemble_stage.layers.1.resize"""    )
						if "pretrained.act_postprocess3.3" in name:
												lowerCamelCase__								=	name.replace("""pretrained.act_postprocess3.3""" ,     """neck.reassemble_stage.layers.2.projection"""    )
						if "pretrained.act_postprocess4.3" in name:
												lowerCamelCase__								=	name.replace("""pretrained.act_postprocess4.3""" ,     """neck.reassemble_stage.layers.3.projection"""    )
						if "pretrained.act_postprocess4.4" in name:
												lowerCamelCase__								=	name.replace("""pretrained.act_postprocess4.4""" ,     """neck.reassemble_stage.layers.3.resize"""    )
						if "pretrained" in name:
												lowerCamelCase__								=	name.replace("""pretrained""" ,     """dpt"""    )
						if "bn" in name:
												lowerCamelCase__								=	name.replace("""bn""" ,     """batch_norm"""    )
						if "head" in name:
												lowerCamelCase__								=	name.replace("""head""" ,     """head.head"""    )
						if "encoder.norm" in name:
												lowerCamelCase__								=	name.replace("""encoder.norm""" ,     """layernorm"""    )
						if "auxlayer" in name:
												lowerCamelCase__								=	name.replace("""auxlayer""" ,     """auxiliary_head.head"""    )

						return name
def     A__      (					__lowerCAmelCase		:      Union[str, Any] ,     __lowerCAmelCase		:      Optional[Any]    ):





						for i in range(config.num_hidden_layers    ):
												# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
												lowerCamelCase__								=	state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.weight'''    )
												lowerCamelCase__								=	state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.bias'''    )
												# next, add query, keys and values (in that order) to the state dict
												lowerCamelCase__								=	in_proj_weight[: config.hidden_size, :]
												lowerCamelCase__								=	in_proj_bias[: config.hidden_size]
												lowerCamelCase__								=	in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												lowerCamelCase__								=	in_proj_bias[
												    config.hidden_size : config.hidden_size * 2
												]
												lowerCamelCase__								=	in_proj_weight[
												    -config.hidden_size :, :
												]
												lowerCamelCase__								=	in_proj_bias[-config.hidden_size :]
def     A__      (					):
						lowerCamelCase__								=	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
						lowerCamelCase__								=	Image.open(requests.get(SCREAMING_SNAKE_CASE__ ,     stream=SCREAMING_SNAKE_CASE__    ).raw    )
						return im
@torch.no_grad()
def     A__      (					__lowerCAmelCase		:      Optional[Any] ,     __lowerCAmelCase		:      List[str] ,     __lowerCAmelCase		:      Tuple ,     __lowerCAmelCase		:      List[Any]    ):
						lowerCamelCase__								=	get_dpt_config(SCREAMING_SNAKE_CASE__    )
						# load original state_dict from URL
						lowerCamelCase__								=	torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__ ,     map_location="""cpu"""    )
						# remove certain keys
						remove_ignore_keys_(SCREAMING_SNAKE_CASE__    )
						# rename keys
						for key in state_dict.copy().keys():
												lowerCamelCase__								=	state_dict.pop(SCREAMING_SNAKE_CASE__    )
												lowerCamelCase__								=	val
						# read in qkv matrices
						read_in_q_k_v(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )

						# load HuggingFace model
						lowerCamelCase__								=	DPTForSemanticSegmentation(SCREAMING_SNAKE_CASE__    ) if """ade""" in checkpoint_url else DPTForDepthEstimation(SCREAMING_SNAKE_CASE__    )
						model.load_state_dict(SCREAMING_SNAKE_CASE__    )
						model.eval()

						# Check outputs on an image
						lowerCamelCase__								=	480 if """ade""" in checkpoint_url else 384
						lowerCamelCase__								=	DPTImageProcessor(size=SCREAMING_SNAKE_CASE__    )

						lowerCamelCase__								=	prepare_img()
						lowerCamelCase__								=	image_processor(SCREAMING_SNAKE_CASE__ ,     return_tensors="""pt"""    )

						# forward pass
						lowerCamelCase__								=	model(**SCREAMING_SNAKE_CASE__    ).logits if """ade""" in checkpoint_url else model(**SCREAMING_SNAKE_CASE__    ).predicted_depth

						# Assert logits
						lowerCamelCase__								=	torch.tensor([[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]    )
						if "ade" in checkpoint_url:
												lowerCamelCase__								=	torch.tensor([[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]    )
						assert outputs.shape == torch.Size(SCREAMING_SNAKE_CASE__    )
						assert (
						    torch.allclose(outputs[0, 0, :3, :3] ,     SCREAMING_SNAKE_CASE__ ,     atol=1e-4    )
						    if "ade" in checkpoint_url
						    else torch.allclose(outputs[0, :3, :3] ,     SCREAMING_SNAKE_CASE__    )
						)

						Path(SCREAMING_SNAKE_CASE__    ).mkdir(exist_ok=SCREAMING_SNAKE_CASE__    )
						print(F'''Saving model to {pytorch_dump_folder_path}'''    )
						model.save_pretrained(SCREAMING_SNAKE_CASE__    )
						print(F'''Saving image processor to {pytorch_dump_folder_path}'''    )
						image_processor.save_pretrained(SCREAMING_SNAKE_CASE__    )

						if push_to_hub:
												print("""Pushing model to hub..."""    )
												model.push_to_hub(
												    repo_path_or_name=Path(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    ) ,     organization="""nielsr""" ,     commit_message="""Add model""" ,     use_temp_dir=SCREAMING_SNAKE_CASE__ ,     )
												image_processor.push_to_hub(
												    repo_path_or_name=Path(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    ) ,     organization="""nielsr""" ,     commit_message="""Add image processor""" ,     use_temp_dir=SCREAMING_SNAKE_CASE__ ,     )


if __name__ == "__main__":
							UpperCamelCase     :   List[str]	      =		argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--checkpoint_url',
							    default='https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt',
							    type=str,
							    help='URL of the original DPT checkpoint you\'d like to convert.',
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path',
							    default=None,
							    type=str,
							    required=True,
							    help='Path to the output PyTorch model directory.',
							)
							parser.add_argument(
							    '--push_to_hub',
							    action='store_true',
							)
							parser.add_argument(
							    '--model_name',
							    default='dpt-large',
							    type=str,
							    help='Name of the model, in case you\'re pushing to the hub.',
							)

							UpperCamelCase     :   List[Any]	      =		parser.parse_args()
							convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)





'''simple docstring'''


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


UpperCAmelCase_						:  int				  =						logging.get_logger(__name__)


if is_vision_available():
				import PIL





class        lowercase__    (		_snake_case   ):





							'''simple docstring'''


							A_				: int       =    ["""pixel_values"""]






							def __init__(       self       ,   __snake_case = True       ,   __snake_case = None       ,   __snake_case = PILImageResampling.BICUBIC       ,   __snake_case = True       ,   __snake_case = None       ,   __snake_case = True       ,   __snake_case = 1 / 255       ,   __snake_case = True       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = True       ,   **__snake_case       ,   ):
											super().__init__(**__snake_case    )
											_SCREAMING_SNAKE_CASE					:			int							= size if size is not None else {"""shortest_edge""": 224}
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= get_size_dict(__snake_case       ,   default_to_square=__snake_case    )
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
											_SCREAMING_SNAKE_CASE					:			Any							= get_size_dict(__snake_case       ,   default_to_square=__snake_case       ,   param_name="""crop_size"""    )

											_SCREAMING_SNAKE_CASE					:			str							= do_resize
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= size
											_SCREAMING_SNAKE_CASE					:			List[str]							= resample
											_SCREAMING_SNAKE_CASE					:			str							= do_center_crop
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= crop_size
											_SCREAMING_SNAKE_CASE					:			Tuple							= do_rescale
											_SCREAMING_SNAKE_CASE					:			Dict							= rescale_factor
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= do_normalize
											_SCREAMING_SNAKE_CASE					:			List[Any]							= image_mean if image_mean is not None else OPENAI_CLIP_MEAN
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= image_std if image_std is not None else OPENAI_CLIP_STD
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= do_convert_rgb






							def UpperCAmelCase_      (       self       ,   __snake_case       ,   __snake_case       ,   __snake_case = PILImageResampling.BICUBIC       ,   __snake_case = None       ,   **__snake_case       ,   ):
											_SCREAMING_SNAKE_CASE					:			int							= get_size_dict(__snake_case       ,   default_to_square=__snake_case    )
											if "shortest_edge" not in size:
															raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""    )
											_SCREAMING_SNAKE_CASE					:			Any							= get_resize_output_image_size(__snake_case       ,   size=size["""shortest_edge"""]       ,   default_to_square=__snake_case    )
											return resize(__snake_case       ,   size=__snake_case       ,   resample=__snake_case       ,   data_format=__snake_case       ,   **__snake_case    )






							def UpperCAmelCase_      (       self       ,   __snake_case       ,   __snake_case       ,   __snake_case = None       ,   **__snake_case       ,   ):
											_SCREAMING_SNAKE_CASE					:			List[str]							= get_size_dict(__snake_case    )
											if "height" not in size or "width" not in size:
															raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""    )
											return center_crop(__snake_case       ,   size=(size["""height"""], size["""width"""])       ,   data_format=__snake_case       ,   **__snake_case    )






							def UpperCAmelCase_      (       self       ,   __snake_case       ,   __snake_case       ,   __snake_case = None       ,   **__snake_case       ,   ):
											return rescale(__snake_case       ,   scale=__snake_case       ,   data_format=__snake_case       ,   **__snake_case    )






							def UpperCAmelCase_      (       self       ,   __snake_case       ,   __snake_case       ,   __snake_case       ,   __snake_case = None       ,   **__snake_case       ,   ):
											return normalize(__snake_case       ,   mean=__snake_case       ,   std=__snake_case       ,   data_format=__snake_case       ,   **__snake_case    )






							def UpperCAmelCase_      (       self       ,   __snake_case       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = None       ,   __snake_case = ChannelDimension.FIRST       ,   **__snake_case       ,   ):
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= do_resize if do_resize is not None else self.do_resize
											_SCREAMING_SNAKE_CASE					:			Any							= size if size is not None else self.size
											_SCREAMING_SNAKE_CASE					:			List[str]							= get_size_dict(__snake_case       ,   param_name="""size"""       ,   default_to_square=__snake_case    )
											_SCREAMING_SNAKE_CASE					:			Any							= resample if resample is not None else self.resample
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= do_center_crop if do_center_crop is not None else self.do_center_crop
											_SCREAMING_SNAKE_CASE					:			str							= crop_size if crop_size is not None else self.crop_size
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= get_size_dict(__snake_case       ,   param_name="""crop_size"""       ,   default_to_square=__snake_case    )
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= do_rescale if do_rescale is not None else self.do_rescale
											_SCREAMING_SNAKE_CASE					:			int							= rescale_factor if rescale_factor is not None else self.rescale_factor
											_SCREAMING_SNAKE_CASE					:			Tuple							= do_normalize if do_normalize is not None else self.do_normalize
											_SCREAMING_SNAKE_CASE					:			List[str]							= image_mean if image_mean is not None else self.image_mean
											_SCREAMING_SNAKE_CASE					:			List[Any]							= image_std if image_std is not None else self.image_std
											_SCREAMING_SNAKE_CASE					:			Any							= do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= make_list_of_images(__snake_case    )

											if not valid_images(__snake_case    ):
															raise ValueError(
															    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
															    """torch.Tensor, tf.Tensor or jax.ndarray."""    )

											if do_resize and size is None:
															raise ValueError("""Size must be specified if do_resize is True."""    )

											if do_center_crop and crop_size is None:
															raise ValueError("""Crop size must be specified if do_center_crop is True."""    )

											if do_rescale and rescale_factor is None:
															raise ValueError("""Rescale factor must be specified if do_rescale is True."""    )

											if do_normalize and (image_mean is None or image_std is None):
															raise ValueError("""Image mean and std must be specified if do_normalize is True."""    )

											# PIL RGBA images are converted to RGB
											if do_convert_rgb:
															_SCREAMING_SNAKE_CASE					:			Optional[int]							= [convert_to_rgb(__snake_case    ) for image in images]

											# All transformations expect numpy arrays.
											_SCREAMING_SNAKE_CASE					:			str							= [to_numpy_array(__snake_case    ) for image in images]

											if do_resize:
															_SCREAMING_SNAKE_CASE					:			Optional[Any]							= [self.resize(image=__snake_case       ,   size=__snake_case       ,   resample=__snake_case    ) for image in images]

											if do_center_crop:
															_SCREAMING_SNAKE_CASE					:			Tuple							= [self.center_crop(image=__snake_case       ,   size=__snake_case    ) for image in images]

											if do_rescale:
															_SCREAMING_SNAKE_CASE					:			List[Any]							= [self.rescale(image=__snake_case       ,   scale=__snake_case    ) for image in images]

											if do_normalize:
															_SCREAMING_SNAKE_CASE					:			Dict							= [self.normalize(image=__snake_case       ,   mean=__snake_case       ,   std=__snake_case    ) for image in images]

											_SCREAMING_SNAKE_CASE					:			str							= [to_channel_dimension_format(__snake_case       ,   __snake_case    ) for image in images]

											_SCREAMING_SNAKE_CASE					:			int							= {"""pixel_values""": images}
											return BatchFeature(data=__snake_case       ,   tensor_type=__snake_case    )




